Text genre identification has a long history of research. [1]} is one of the first works on text genre classification containing a comparison of various datasets, models, and data features. It shows how traditional ML models such as SVM with various hyper-parameters and features performs for the genre classification of texts. Since then, traditional ML models have been superseded by neural models. [2]} introduced BERT – (Bidirectional Encoder Representations from Transformers), an efficient language representation model based on the Transformer architecture [3]}. It achieves state-of-the-art results for various NLP tasks, including text classification. XLM-RoBERTa [4]} is an improved variant of BERT. It has a similar architecture but uses a bigger and more genre-diverse corpus based on Common Crawl (instead of Wikipedia for the multilingual BERT). Therefore, we choose XLM-RoBERTa as the classifier for the experiments in our research.
